Coordinative structuring of gait kinematics during adaptation to variable and asymmetric split-belt treadmill walking – A principal component analysis approach

Hinkel-Lipsker, Jacob W.; Hahn, Michael E.

doi:10.1016/j.humov.2018.04.009

Cited by 12 publications

(8 citation statements)

References 42 publications

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“…Such procedure was applied given the intent to compare different tasks (walking and running) 24 or include different types of input variables (e.g., ground reaction force and positional data) 26 . Nevertheless, the explained variance in our study (~ 90%) is similar to others that applied the same PCA procedure 20,39 .…”

Section: Discussionsupporting

confidence: 88%

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Perceiving amputee gait from biological motion: kinematics cues and effect of experience level

Lee¹,

Pacheco²,

Lewek³

et al. 2020

Sci Rep

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Physical therapists (PT) and clinicians must be skilled in identifying gait features through observation to assess motor deficits in patients and intervene appropriately. Inconsistent results in the literature have led researchers to question how clinical experience influences PT’s gait perception and to seek the key kinematic features that should be trained to enhance PT’s skill. Thus, this study investigated (1) what are the informative kinematic features that allow gait-deviation perception in amputee gait and (2) whether there are differences in observational gait skills between PT and individuals with less clinical experience (PT students [PTS] and Novices). We introduced a new method that combines biological motion and principal component analysis to gradually mesh amputee and typical walking patterns. Our analysis showed that on average the accuracy rate in identifying gait deviations between PT and PTS was similar and better than Novices. Also, we found that PT’s experience was demonstrated by their better perception of gait asymmetry. The extracted principal components demonstrated that the major gait deviation of amputees was the medial–lateral body sway and spatial gait asymmetry.

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Section: Discussionsupporting

confidence: 88%

“…4 and 5 ) and thus provide a holistic viewpoint when interpreting the results. This approach has been used in gait analysis 20 , 24 – 26 , 39 . Some of these studies reported lower explained variance in the first 4 PCs 24 , 25 when analyzing healthy individuals compared to our results.…”

Section: Discussionmentioning

confidence: 99%

Perceiving amputee gait from biological motion: kinematics cues and effect of experience level

Lee¹,

Pacheco²,

Lewek³

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In the case of classification, authors use, for example, HMM [23], DTW [24], nearest neighbors [25][26][27], and SVM [28]. Authors also perform various types of analysis for example aligning signals with Two Step Aligning algorithm [29], drawing direct conclusions from principal components [30][31][32][33] or data segmentation [34]. The second application of PCA is for generating motion features of fixed size that are vector of real values which represents features of linear combination of eigenvectors that can be also classified by general-purpose classifiers like, for example, SVM [35][36][37] or nearest neighbors [38].…”

Section: Effective Methods Of Human Motion Analysis and Classificationmentioning

confidence: 99%

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

Hachaj

Piekarczyk

2019

Sensors

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The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an internal measurement unit (IMU) sensor that has 6-axis accelerometer and gyroscope. We will validate a classifier that uses Principal Components Analysis (PCA)-based features with various numbers of dimensions, a two-stage PCA-based method, a feedforward artificial neural network, and random forest. Moreover, we will also propose a Dynamic Time Warping (DTW) classifier trained with extension of DTW Barycenter Averaging (DBA) algorithm that utilizes quaternion averaging and a bagged variation of previous method (DTWb) that utilizes many DTW classifiers that perform voting. The evaluation has been performed on 975 head gesture recordings in seven classes acquired from 12 persons. The highest value of recognition rate in a leave-one-out test has been obtained for DTWb and it equals 0.975 (0.026 better than the best of state-of-the-art methods to which we have compared our approach). Among the most important applications of the proposed method is improving life quality for people who are disabled below the neck by supporting, for example, an assistive autonomous power chair with a head gesture interface or remote controlled interfaces in robotics.

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“…In [15], PCA was applied to compare lower-body kinematics during loaded walking compared with unloaded walking. The authors of [16] used PCA to reduce the feature space in the kinematic analysis of gait while the participants adapted to variable and asymmetric split-belt treadmill walking. In [17], radial basis functions (RBFs) and PCA were used to model and extract stylistic and affective features from motion data.…”

Section: Recent Work In the Application Of Pca For Human Motion Analysismentioning

confidence: 99%

Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

Hachaj

2019

Symmetry

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This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “weak classifier” (it does not randomly sample a feature set). The proposed classification method was evaluated on a challenging (even to a human observer) motion capture recording dataset of martial arts techniques performed by professional karate sportspeople. The dataset consisted of 360 recordings in 12 motion classes. Because some classes of these motions might be symmetrical (which means that they are performed with a dominant left or right hand/leg), an analysis was conducted to determine whether accounting for symmetry could improve the recognition rate of a classifier. The experimental results show that applying the proposed classifiers’ bagging procedure increased the recognition rate (RR) of the Nearest-Neighbor (NNg) and Support Vector Machine (SVM) classifiers by more than 5% and 3%, respectively. The RR of one trained classifier (SVM) was higher when we did not use symmetry. On the other hand, the application of symmetry information for bagged NNg improved its recognition rate compared with the results without symmetry information. We can conclude that symmetry information might be helpful in situations in which it is not possible to optimize the decision borders of the classifier (for example, when we do not have direct information about class labels). The experiment presented in this paper shows that, in this case, bagging and mirroring might help find a similar object in the training set that shares the same class label. Both the dataset that was used for the evaluation and the implementation of the proposed method can be downloaded, so the experiment is easily reproducible.

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Coordinative structuring of gait kinematics during adaptation to variable and asymmetric split-belt treadmill walking – A principal component analysis approach

Cited by 12 publications

References 42 publications

Perceiving amputee gait from biological motion: kinematics cues and effect of experience level

Perceiving amputee gait from biological motion: kinematics cues and effect of experience level

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

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